Speech Recognition Using Loosely Coupled Components

ABSTRACT

An automatic speech recognition system includes an audio capture component, a speech recognition processing component, and a result processing component which are distributed among two or more logical devices and/or two or more physical devices. In particular, the audio capture component may be located on a different logical device and/or physical device from the result processing component. For example, the audio capture component may be on a computer connected to a microphone into which a user speaks, while the result processing component may be on a terminal server which receives speech recognition results from a speech recognition processing server.

BACKGROUND

A variety of automatic speech recognition (ASR) systems exist forrecognizing speech to perform functions such as creating transcripts ofthe speech and controlling the operation of a computer. In one commonconfiguration for such systems, a microphone is connected directly to adesktop computer or other computing device which executes automaticspeech recognition software for recognizing the user's speech and actingon the results of that recognition. In another common configuration ofsuch systems, the user makes a telephone call and speaks into atelephone, and an automatic speech recognition system remote from theuser recognizes the user's speech and acts on the results of thatrecognition.

Recently a much wider variety of computing devices have become availablehaving varying features and costs. For example, in addition to desktopand laptop computers (which typically must be connected to an externalmicrophone, purchased separately, to capture speech), vendors nowprovide a wide variety of personal digital assistants (PDAs),smartphones, and tablet computers, all of which are capable ofconnecting to the Internet and other networks (often wirelessly), all ofwhich are capable of executing custom applications to some extent, andsome of which contain built-in microphones.

What is needed, therefore, are improved techniques for making use of avariety of computing technologies to provide automatic speechrecognition capabilities that provide the right combination ofrecognition quality, recognition speed, and cost of ownership.

SUMMARY

An automatic speech recognition system includes an audio capturecomponent, a speech recognition processing component, and a resultprocessing component which are distributed among two or more logicaldevices and/or two or more physical devices. In particular, the audiocapture component may be located on a different logical device and/orphysical device from the result processing component. For example, theaudio capture component may be on a computer connected to a microphoneinto which a user speaks, while the result processing component may beon a terminal server which receives speech recognition results from aspeech recognition processing server.

In another embodiment, the audio capture component may be on the samelogical device and/or physical device as the result processingcomponent, but the effects of applying the speech recognition resultsmay be output (e.g., displayed) to the user through a different logicaldevice and/or physical device, such as a computer connected to aterminal server. In this embodiment, the end user experience is similarto that in which the audio capture component and/or result processingcomponent are located on the user's computer, even though in factneither such component is located on the user's computer.

In one embodiment, a system comprises: a first device including an audiocapture component, the audio capture component comprising means forcapturing an audio signal representing speech of a user to produce acaptured audio signal; a speech recognition processing componentcomprising means for performing automatic speech recognition on thecaptured audio signal to produce speech recognition results; a seconddevice including a result processing component; and a context sharingcomponent comprising: means for determining that the result processingcomponent is associated with a current context of the user; wherein theresult processing component comprises means for processing the speechrecognition results to produce result output.

In another embodiment, a method is performed by at least one processorexecuting computer program instructions stored on a non-transitorycomputer-readable medium. The method is for use with a system, whereinthe system comprises: a first device including an audio capturecomponent; a speech recognition processing component; and a seconddevice including a result processing component. The method comprises:(A) using the audio capture component to capture an audio signalrepresenting speech of a user to produce a captured audio signal; (B)using the speech recognition processing component to perform automaticspeech recognition on the captured audio signal to produce speechrecognition results; (C) determining that the result processingcomponent is associated with a current context of the user; (D) inresponse to the determination that the result processing component isassociated with the current context of the user, providing the speechrecognition results to the result processing component; and (E) usingthe result processing component to process the speech recognitionresults to produce result output.

In another embodiment, a system comprises: an audio capture component,the audio capture component comprising means for capturing a first audiosignal representing first speech of a user to produce a first capturedaudio signal; a speech recognition processing component comprising meansfor performing automatic speech recognition on the first captured audiosignal to produce first speech recognition results;

a first result processing component, the first result processingcomponent comprising first means for processing the first speechrecognition results to produce first result output; a second resultprocessing component, the second result processing component comprisingsecond means for processing the first speech recognition results toproduce second result output; a context sharing component comprisingmeans for identifying a first one of the first and second resultprocessing components as being associated with a first context of theuser at a first time; and speech recognition result provision means forproviding the first speech recognition results to the identified firstone of the first and second result processing components.

Other features and advantages of various aspects and embodiments of thepresent invention will become apparent from the following descriptionand from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1D are diagrams of prior art systems for performing automaticspeech recognition;

FIGS. 2A-2B are diagrams of systems for performing automatic speechrecognition using distributed components according to variousembodiments of the present invention;

FIG. 3 is a flowchart of a method performed by the systems of FIGS.2A-2B;

FIG. 4 is a diagram of a system for identifying the current context of auser according to one embodiment of the present invention;

FIG. 5 is a flowchart of a method performed by the system of FIG. 4according to one embodiment of the present invention; and

FIGS. 6A-6B are diagrams of data structures representing the currentcontext of a user at different times according to one embodiment of thepresent invention.

DETAILED DESCRIPTION

Referring to FIG. 1A, an example of a prior art automatic speechrecognition (ASR) system 100 a is shown. The system 100 a illustratesvarious features which are useful for understanding the characteristicsand limitations of conventional ASR systems. For example, in the system100 a, a user 102 speaks into a microphone 106 that is connected to adesktop computer 110 a. The microphone 106 is an example of an “audiocapture device” as that term is used herein. The microphone 106 capturesthe user's speech 104 and generates as output an audio signal 108representing the speech 104. The original audio signal will be referredto herein as the “original audio signal” to distinguish it from thecaptured audio signal 114 described below. The microphone 106 transmitsthe original audio signal 108 to the desktop computer 110 a, such asover a wired or wireless connection.

Although the microphone 106 may be a dedicated external microphone, itmay instead, for example, be contained within a digital voice recorder,cellular telephone, or other device containing one or more modules thatare capable of performing functions other than capturing speech. In anysuch case, however, the microphone 106 transmits the original audiosignal 108 to the desktop computer 110 a for processing by the desktopcomputer 110 a, as will now be described.

The desktop computer 110 a includes an audio capture (AC) component 112which receives the original audio signal 108. The audio capturecomponent 112 may process the original audio signal 108 into a formsuitable for transmission, such as by compressing and/or performingpre-processing on the original audio signal 108. The audio capturecomponent 112 outputs a captured audio signal 114, which may be the sameas the original audio signal 108, or which may differ from the originalaudio signal 108 if the audio captured component 112 applied anyprocessing to the original audio signal 108.

The desktop computer 110 a also includes a speech recognition processing(SRP) component 116. The audio capture component 112 transmits thecaptured audio signal 114 to the speech recognition processing component116. Since, in the example shown in FIG. 1A, the audio capture component112 and the speech recognition processing component 116 are on the samedesktop computer 110 a, the audio capture component 112 may, forexample, transmit the captured audio signal 114 to the speechrecognition processing component 116 using a local function call.

The speech recognition processing component 116 applies automatic speechrecognition to the captured audio signal 114 and, as a result, producesspeech recognition results 118. The results 118 may include, forexample, the text of a dictated sentence recognized from the audiosignal 114 (e.g., “the patient arrived complaining of headaches”), or acommand (e.g., “close window” or “connect me with the operator”)recognized from the audio signal 114.

The desktop computer 110 a also includes a result processing (RP)component 120. The speech recognition processing component 116 transmitsthe speech recognition results 118 to the result processing component120, which receives the speech recognition results 118 and takesappropriate action in response to the speech recognition results 118,thereby producing result output 122. An output device 124 connected tothe desktop computer 110 a may display output 126 to the user 102 whichrepresents the result output 122.

Although not shown in FIG. 1A for ease of illustration, the resultprocessing component 120 may provide the result output 122 to anapplication, such as a word processor, executing on the desktop computer110 a. For example, if the speech recognition results 118 are a sentenceof text, the result processing component 120 may cause the wordprocessor to insert such text into an open document at the current textcursor location. The output device 124 may then display the updateddocument as the user output 126. As another example, if the speechrecognition results 118 are a command (e.g., “close window”), then theresult processing component 120 may execute the command (such as byclosing the foreground window), and the output device 124 may thenprovide an updated display in which the foreground window does notappear.

The audio capture component 112, speech recognition processing component116, and result processing component 120 are all examples of “speechrecognition components” as that term is used herein. Therefore anyreference to a “speech recognition component” herein shall be understoodto refer to one or more of an audio capture component, a speechrecognition processing component, and a result processing component.

Although the computer 110 a in the system 100 a of FIG. 1A need not be adesktop computer, in many cases the computer 110 a is a desktop computerwith a relatively powerful processor and a relatively large amount ofmemory in order to meet the substantial computing resource requirementsof the speech recognition processing component 116. Alternatively, forexample, the speech recognition processing component 116 may beoffloaded to a remote speech recognition server, as shown in the system100 b FIG. 1B.

As yet another example, the microphone 106 (or other audio capturedevice) may be associated with a client session of a terminal server(such as a Citrix server), as shown in the system 100 c of FIG. 1C. Morespecifically, in FIG. 1C, the desktop computer 100 c no longer containsthe speech recognition processing component 116 or the result processingcomponent 120. Instead, although the desktop computer 110 c contains anaudio capture component 109 for at least partially capturing the audiosignal 108, the terminal server 130 a also contains both audio capturecomponent 112 and speech recognition processing component 116 and resultprocessing component 120.

The computer 110 c in the system 100 c of FIG. 1C now contains terminalviewer software 132 (such as Citrix client software) for establishingand maintaining a terminal session connection with the terminal server130 a. As a result, the terminal viewer software 132 essentially passesuser input (such as the original audio signal 108) directly to aterminal session manager 134 on the terminal server 130 a. The terminalsession manager 134 then handles the input received from the terminalservices client 132 appropriately, such as by forwarding the originalaudio signal 108 to the audio capture component 112. Similarly, theterminal session manager 134 transmits output (such as the result output122) to the terminal services client 132, which in turn forwards theoutput to the appropriate output device connected to the desktopcomputer 110 c (e.g., the output device).

From the perspective of the user 102, operation of the system 100 c ofFIG. 1C appears similar to that of the system 100 a of FIG. 1A, eventhough in the components 112, 116, and 120 reside on the desktopcomputer 100 a in FIG. 1A and on the terminal server 130 a in FIG. 1C.In the configuration of FIG. 1C, the terminal services client 132essentially acts as a two-way window into the terminal server 130 a,such that the terminal server 130 a performs all functions of thecomponents 112, 116, and 118, but merely uses the terminal servicesclient 132 on the desktop computer 110 c to obtain input from the user102 and to provide output to the user 102. Reasons for using theconfiguration of FIG. 1C include security (because loss, theft, or othercomprise of the computer 110 c does not comprise and applications and/ordata on the server 130 a) and processing efficiency (because a singlehigh-powered terminal server can provide fast, high-quality speechrecognition for many small, mobile, low-powered client computers).

Just as the speech recognition processing component 116 of FIG. 1A maybe offloaded from the desktop computer 100 a of FIG. 1A to the remotespeech recognition server 128 of FIG. 1B, so too may the speechrecognition processing component 116 of FIG. 1C be offloaded from theterminal server 130 a of FIG. 1C to produce the system 100 d shown inFIG. 1D. The interaction between the terminal server 130 b and thespeech recognition server 128 in the system 100 d of FIG. 1D, therefore,is similar to that of the interaction between the desktop computer 110 band the speech recognition server 128 of the system 100 b of FIG. 1B.

As yet another example, consider the system 100 d of FIG. 1D, but inwhich the microphone 106 is replaced by a telephone, embedded within atelephone, or used as a telephone input device. For example, thetelephone may be a conventional analog telephone, in which case thedesktop computer 110 c may instead be a telephony server connected tothe telephone over an analog telephone connection. The terminal server130 b may instead be a dialogue processing server connected to thetelephony server. Otherwise, the components of the system 100 d mayperform the same general functions as those described above with respectto FIG. 1D. Specifically, the dialogue processing server may include theaudio capture component 112 and the result processing component 120,while the speech recognition server 128 may include the speechrecognition processing component 116.

Alternatively, for example, the microphone 106 and/or telephone may beor otherwise act as a Voice over Internet Protocol (VoIP) telephone, inwhich case the telephony server is optional. The telephone may, forexample, connect to the dialogue processing server without the use of atelephony server.

In all of the cases disclosed above, the result processing component 120is logically located on the same device as the audio capture component112. Even in the cases (FIGS. 1C and 1D) in which the computer 110 cincludes terminal services client 132 and the audio capture device 106is connected to the result processing component 120 over a network, theaudio capture component 112 still resides on the same device (namely,the computer 110 c) as the result processing component 120. In all ofthese cases, therefore, the audio capture component 112 is physicallyand/or logically located on the same device as the result processingcomponent 120.

In contrast, embodiments of the present invention separate the audiocapture component from the result processing component, so that the twocomponents reside on different physical and/or logical devices than eachother.

However, in embodiments of the present invention, the audio capturecomponent is still in communication with the result processing componentthrough a loose, dynamic coupling. Such a coupling may take any of avariety of forms and be established in any of a variety of ways. Forexample, the coupling between the audio capture component and the resultprocessing component may be established at runtime by discovering andmatching the application context of both components. Once such contextdiscovery and matching is performed, the shared context of the audiocapture component and result processing component may be used to enablethe two components to communicate with each other by sending controland/or data signals to each other.

For example, referring to FIG. 2A, a dataflow diagram is shown of asystem 200 a implemented according to one embodiment of the presentinvention. Referring to FIG. 3, a flowchart is shown of a method 300performed by the system 200 a of FIG. 2A according to one embodiment ofthe present invention.

The system 200 a of FIG. 2A includes various components which may beimplemented in the same or similar manner to components of the systemsof FIGS. 1A-1D. Therefore, in the discussion that follows, it should beassumed that individual components of the system 200 a of FIG. 2A (andof other embodiments of the present invention) may be implemented in thesame or similar manner as corresponding components in the systems 200a-d of FIGS. 1A-1D, respectively, unless stated otherwise.

For example, in the system 200 a of FIG. 2A, user 202 speaks intomicrophone 206 (or other audio capture device) connected to desktopcomputer 210 a. The microphone 206 captures the user's speech 204 andgenerates as output an original audio signal 208, which represents thespeech 204 (FIG. 3, operation 302). The microphone 206 transmits orotherwise provides the original audio signal 208 to audio capturecomponent 212, which in FIG. 2A is located on desktop computer 210 a(FIG. 3, operation 304). As illustrated by the example in FIG. 2A, theaudio capture component 212 is the first component in the system 200 ato receive the audio signal 208 after the audio signal is output by themicrophone 206.

The audio capture component 212 receives the original audio signal 208and produces as output the captured audio signal 214 based on theoriginal audio signal 208 (FIG. 3, operation 306). The audio capturecomponent 212 may, for example, produce the captured audio signal 214 inany of the ways described above with respect to FIGS. 1A-1D.

Unlike the systems of FIGS. 1A-1D, however, the desktop computer 210 aof the system 200 a of FIG. 2A does not include result processingcomponent 220. Instead, the result processing component 220 is locatedon a terminal server 230 a in the system 200 a of FIG. 2A. Speechrecognition results 218 are generated and provided to the resultprocessing component 220 in the system 200 a of FIG. 2A as follows.

Audio capture component 212 provides the captured audio signal 214,e.g., by transmitting it over a network connection, to the speechrecognition processing component 216, which in the system 200 a of FIG.2A is located on a remote speech recognition server 228 (FIG. 3,operation 308). The speech recognition processing component 216 receivesthe captured audio signal 214 and performs automatic speech recognitionon the captured audio signal 214 to produce speech recognition results218 (FIG. 3, operation 310).

The speech recognition results 218, once produced, must be provided tothe result processing component 220. In the system 200 a of FIG. 2A,however, the result processing component 220 is not located on the samedevice as the speech recognition processing component 216. Furthermore,as will be described in more detail below, the location of the resultprocessing component 220 may be dynamic, e.g., the location of theresult processing component 220 may vary over time in response tochanges in the current context of the user 202.

Because the location of the result processing component 220 is dynamicin the system 200 a of FIG. 2A, the speech recognition processingcomponent 216 cannot rely on conventional techniques, such as a localmethod call or a call to a predetermined address, to provide the speechrecognition results 218 to the result processing component 220. Instead,to provide the speech recognition results 218 to the result processingcomponent 220 in the system 200 a of FIG. 2A, it is necessary toidentify the current location of the result processing componentcurrently associated with the user 202 so that the speech recognitionresults 218 may be provided to that result processing component. In theembodiment illustrated in FIG. 2A, a context sharing component 250 inthe system 200 a identifies the result processing component 220 to whichthe speech recognition results 218 should be provided, which mayinclude, for example, identifying a location (e.g., IP address) of theresult processing component 220 and/or a method of providing the results218 to the result processing component 220 (e.g., local procedure callif the speech recognition processing component 216 is located on thesame device as the result processing component 220, or network if thespeech recognition processing component 216 is located on a differentdevice than the result processing component 220). The context sharingcomponent 250 may identify such information about the appropriate resultprocessing component 220 based on a current context 252 of the user 202(FIG. 3, operation 312). Examples of the user's current context 252, andhow the current context record 252 may be generated and managed, areprovided below.

Assume for purposes of example that the context sharing component 250identifies the result processing component 220 as the result processingcomponent currently associated with the user 202. In response to such anidentification, the speech recognition results 218 are provided (e.g.,transmitted over a network) to the result processing component 220 (FIG.3, operation 314).

The speech recognition results 218 may be provided to the resultprocessing component 220 in any of a variety of ways. For example, thespeech recognition processing component 216 may request that the contextsharing component 250 identify a result processing component currentlyassociated with the user 202. To enable the context sharing component250 to identify such a result processing component, the speechrecognition processing component 216 may, for example, provide thecontext sharing component 250 with information about the user 202, suchas information derived from a current session between the user'scomputer 210 a and the speech recognition server 228.

In response to such a request, the context sharing component mayidentify a result processing component currently associated with theuser 202, and provide information identifying the result processingcomponent to the speech recognition processing component 216. The speechrecognition processing component 216 (or other component of the speechrecognition server 228) may use such information to transmit the speechrecognition results 218 to the identified result processing component220. As another example, the speech recognition processing component 216may provide the speech recognition results 218 to the context sharingcomponent 250 (e.g., as part of the request to identify a resultprocessing component associated with the user 202), and the contextsharing component 250 may in turn provide the speech recognition results218 to the identified result processing component 220 after identifyingthe result processing component 250.

Once the result processing component 220 receives the speech recognitionresults 218, the speech recognition results 218 may be processed by theresult processing component 220 and other components of the system 200 ain the same or similar manner to that described above with respect toFIGS. 1A-1D to produce result output (FIG. 3, operation 316). Ingeneral, the result processing component 220 may provide the resultoutput 222 to an application 240 executing on the terminal server 230 ain a session of the terminal session manager 234. If the results 218include text or other data output representing content of the speech204, then the application 240 may process the results 218 by, forexample, inserting the results 218 into a document as part of atranscription process. As another example, if the results 218 includeone or more commands, then the application 240 may process the results218 by executing the commands to perform functions such as closingwindows, opening files, or executing software.

Such actions performed by the application 240 are examples of actionsthat may change the current state of the application. For example,inserting a word into an open document of the target application 240 maychange the state of the application in various ways, such as by changingthe contents of the document and changing the position of the textcursor within that document. The result processing component 220 mayobtain application state data 242 from the target application 240. Theapplication state data 242 may, for example, include data reflecting achange in the state of the target application 240 resulting fromprocessing of the result output 222 by the target application 240. Thetarget application 240 may, for example, push the application state data242 to the result processing component 220 upon a change of state in theapplication 240 or, as another example, the result processing component220 may obtain the application state data 242 from the application 240in response to a request from the result processing component 220 forsuch data 242.

The result processing component 220 may inform the speech recognitionprocessing component 216 of the state of the target application 240. Forexample, after receiving the application state data 242 from the targetapplication 240, the result processing component 220 may transmit anapplication state message 244 to the speech recognition processingcomponent 216. The application state message 244 may, for example,represent the same data as the application state data 242, but may takea different form. The result processing component 220 may, for example,push the application state message 244 to the speech recognitionprocessing component 216 in response to receiving the application statedata 242 from the application 240 or, as another example, the speechrecognition processing component 216 may receive the application statemessage 244 from the target application 240 in response to a requestfrom the speech recognition processing component 216 for such a message244. The application state message 244 may, for example, be transmittedover a network using any network protocol.

The speech recognition processing component 216 may take any appropriateaction in response to and based on the content of the application statemessage 244. For example, the speech recognition processing component216 may change any aspect of its speech recognition context (e.g., thecurrent acoustic model and/or language model) in response to and basedon the content of the application state message 244. For example, if theapplication state message 244 indicates that the application 240currently is displaying a particular dialog box, then in response to themessage 244 the speech recognition processing component 216 may changeits language model to reflect the user interface elements (e.g.,buttons) contained within the dialog box.

The target application 240 may be any software executing on the terminalserver 230 a in a session of the terminal session manager 234. Thetarget application 240 need not be an application program and may, forexample, be an operating system or other non-application software.Therefore, another example of an application state change that may bereflected in the application state message 244 is a switch in foregroundfrom one application to another within the same operating system, inresponse to which the speech recognition processing component 216 maychange any aspect of its speech recognition context (e.g., the currentacoustic model and/or language model) to reflect the applicationcurrently in the foreground.

Although the application state message 244 is shown in FIG. 2A as beingtransmitted directly by the result processing component 220 to thespeech recognition processing component 216, alternatively the resultprocessing component 220 may transmit the application state message 244to the context sharing component 250, which may then transmit theapplication state message 244 (or any data contained within theapplication state message 244) to the speech recognition processingcomponent 216 using any of the techniques disclosed herein. For example,after text has been inserted into a document by the application 240(whether as a result of transcribing speech or by other means, such astyping the text directly into the document), the result processingcomponent 220 may inform the context sharing component 250 of the newstate of the application 240 by transmitting the application statemessage 244 to the context sharing component 250. The context sharingcomponent 250 may store a record of this state change, and eitherproactively forward such a record to the speech processing component216, or make the record available for retrieval by the speechrecognition processing component 216. This is merely one example of away in which the context sharing component 250 may act as a registry forstoring information provided by speech recognition components, and formaking such stored information available to the speech recognitioncomponents. One benefit of this feature of the context sharing component250 is that it enables the various speech recognition components tocommunicate with each other despite the lack of traditionalcommunication mechanisms (e.g., local procedure calls) which would beavailable to the speech recognition components if they were all residentand executing on the same logical and/or physical machine.

In the system 200 a of FIG. 2A, the computer 210 a contains a terminalservices client 232 (such as Citrix client software) for establishingand maintaining a terminal session connection with the terminal sessionmanager 234 on the terminal server 230 a. The terminal session manager234 may receive and transmit the result output 222 to the terminalservices client 232 (FIG. 3, operation 318), which in turn may forwardthe result output 222 to an appropriate output device 224 connected tothe desktop computer 210 a to provide output 226 to the user 202 (e.g.,a computer monitor) (FIG. 3, operation 320). If the results 218 includetext, for example, which is inserted into a document, the user outputdevice 224 may display the updated document as the user output 226. Asanother example, if the results 218 include a command for launching anapplication program, then the user output device 224 may display theapplication program window(s) as the user output 226.

The system 200 a of FIG. 2A has a variety of advantages over the priorart systems illustrated in FIGS. 1A-1D. For example, the audio capturecomponent 212 on the desktop computer 210 a in FIG. 2A may be a softwareapplication installed on the desktop computer 210 a for capturing theaudio signal 208 from the microphone 206. Compare this to the system 100d of FIG. 1D, in which the audio capture component 112 is on theterminal server 130 b. In FIG. 1D, because the original audio signal 108must first be transmitted over a network connection from the desktopcomputer 110 c to the terminal server 130 b before being captured by theaudio capture component 112, the resulting captured audio signal 114 maybe suboptimal because, for example, the original audio signal 108 mayexperience loss or other degradation in transmission from the desktopcomputer 110 c to the terminal server 130 b. Such degradation is commonin conventional systems because the audio signal 114 is compressed in away that is optimized for playback to a human. The resulting compressedaudio signal is of lower quality than is needed for optimal speechrecognition. In contrast, in the embodiment illustrated in FIG. 2A, thecaptured audio signal 214 produced by the audio capture component 212may be optimized for the purpose of speech recognition, therebyimproving the quality of the speech recognition results 218.

Despite the separation of the audio capture component 212 and the resultprocessing component 220 in the embodiment of FIG. 2A, the resultprocessing component 220 may still process the speech recognitionresults 218 to produce the result output 222 in real-time orsubstantially in real-time. Similarly, the system 200 a may process thespeech 204 to produce the result output 222 and provide the resultoutput 222 to the target application 240 in real-time or substantiallyin real-time. As a result, the combined process of recognizing thespeech 204, processing the speech recognition results 218 to produce theresult output 222, and providing the result output 222 to the targetapplication 240 may be performed in real-time or substantially inreal-time. Similarly, the result processing component 220 may receivethe application state data 242 and provide the application state message244 to the speech recognition processing component 216 in real-time orsubstantially in real-time. Although some delay may be introducedbetween utterance of the speech 204 and generation of result output 222,such delay may be due solely to delays inherent in the operation ofcomponents such as the audio capture component 212, speech recognitionprocessing component 216, and result processing component 220, andtransmission delays between them, not due to any storage of the audiosignal 208, captured audio signal 214, or speech recognition results 218while waiting for action to be taken by a human operator, such as atranscriptionist. Instead, each of the components 212, 216, and 220 maygenerate its output as quickly as possible upon receipt of its input,and may provide its output to the consumer of such output immediatelyupon generating such output.

The audio capture component 212 and result processing component 220 maybe separated from each other onto different physical and/or logicaldevices in ways other than that illustrated in FIG. 2A. For example, inthe system 200 b of FIG. 2B, the user 202 speaks into a telephone 207(which may contain a microphone or other audio capture device) toproduce audio signal 208. The audio signal 208 is provided to speechrecognition server 228, which may also function as a telephony server inthe system 200 b of FIG. 2B. The server 228 includes an audio capturecomponent 213, which may receive the audio signal 208 from a VoIPcomponent of the server 228 and produce captured audio signal 214. Notethat in FIG. 2B the telephone 207 is not directly coupled to the desktopcomputer 210 b, whether by a wired connection or a wireless (e.g.,Bluetooth) connection. As a result, the audio capture component 212 isthe first component in the system 200 b to receive the audio signal 208after the audio signal is output by the device (namely, the telephone207) which outputs the audio signal 208 based on the speech 204 of theuser. The system 200 b of FIG. 2B may operate in other respects in thesame manner as the system 200 a of FIG. 2A.

The system 200 b of FIG. 2B is another example of an embodiment of thepresent invention in which the audio capture component 212 is located ona different physical and/or logical device from the result processingcomponent 220. Furthermore, in the system 200 b of FIG. 2B, the computer210 b, containing the terminal services client 232, is a distinctphysical and/or logical device from that which contains the audiocapture component 212. One benefit of the system 200 b of FIG. 2B isthat it enables speech recognition to be performed using a computingdevice, such as the desktop computer 210 b, even when there is nomicrophone connected locally to that computing device.

Even when the computer 210 b is not well-suited for the audio capturecomponent 212 (e.g., because it is not locally connected to amicrophone), the computer 210 b may be well-suited to execute theterminal services client 232. For example, the computer 210 b maycontain sufficient computational resources to execute the terminalservices client 232 effectively, and may also contain a full-sizedkeyboard and mouse for receiving input from the user 202, a full-sizedmonitor for displaying output 226 to the user 202, and a high-speedwired network connection for communicating with the terminal server 230a. As a result, the configuration illustrated in FIG. 2B, in which theaudio capture component 212 is located on the speech recognition server228 and the terminal services client 232 is located on the computer 210b, may distribute the audio capture component 212 and the terminalservices client 232 so that each such component is located and executedby the device best suited for it.

In the system 200 b of FIG. 2B, the user 202 can take full advantage ofthe significant processing power of the speech recognition server 228,while taking full advantage of the input and output capabilities of thecomputer 210 b. For example, the user 202 may speak into the microphone206 (e.g., a microphone contained within or connected to (by wire orwirelessly) a VoIP telephone or cellular telephone) and experience theresults of applying speech recognition to the speech 204 rapidly, e.g.,in real-time. For example, the user 202 may dictate into the microphone206 and, in response, the terminal services client 232 may displaytranscribed text (as output 226) corresponding to the user's speech 204in real-time. As another example, the user 202 may speak commands intothe microphone 206 and, in response, the terminal services client 232may display the results (as output 226) of executing those commands.From the user's perspective, such an experience may be similar to orindistinguishable from the experience of using an automatic speechrecognition system located on the desktop computer 210 b itself, eventhough in fact the components 112, 116, and 120 of the speechrecognition system are located remotely on the speech recognition server228, and even though the microphone 206 is not connected to the desktopcomputer 210 b.

As mentioned above, the context sharing component 250 may store,maintain, or otherwise access and make use of the current context 252 ofthe user 202 when connecting the audio capture component 212, speechrecognition processing component 216, and result processing component220 with each other. For example, the current context 252 of the user202 may indicate which audio capture component(s), speech recognitionprocessing component(s), and result processing component(s) arecurrently being used, or otherwise available for use, by the user 202.Such components may include, for example, any such components which arelocated on devices currently being used by the user 202.

The context sharing component 250 may generate, store, maintain, and/oraccess a record 252 of the user's current context. Such a record 252 mayrepresent the user's context at a particular point in time. Therefore,as described in more detail below, the context record 252 associatedwith the user 202 may change over time as the user 202 starts using newdevices (or otherwise obtains access to new speech recognitioncomponents) and as the user 202 stops using previously-used devices (orotherwise terminates access to speech recognition components).

For example, referring to FIG. 6A, an example is shown of a user contextrecord 600 a according to one embodiment of the present invention. Thecontext record 600 a in FIG. 6A is an example of the current usercontext record 252 shown in FIGS. 2A and 2B. The context record 600 aincludes an identifier 602 of the user 202. The identifier 602 mayinclude any data which distinguishes the user 202 from other users,and/or which enables the user 202 to be located and/or communicatedwith. For example, the user identifier 602 may include any one or moreof a user name, password, email address, Internet Protocol (IP) address,and session identifier (such as a session identifier of a session of theuser 202 with the terminal server 230 a-b, the speech recognition server228, or the context sharing component 250).

The context record 600 a also includes lists of audio capture components604, speech recognition processing components 608, and result processingcomponents 612 located and executing on devices currently being used bythe user 202, or which otherwise are currently authorized for use by oron behalf of the user 202. In the particular example illustrated in FIG.6A, the audio capture component list 604 lists exactly one audio capturecomponent (in element 606 a), the speech recognition processingcomponent list 608 lists exactly one speech recognition processingcomponent (in element 610 a), and the result processing component list612 lists exactly one result processing component (in element 614 a).However, any of the lists 604, 608, and 612 may contain any zero, one,or more speech recognition components at any time. For example, thelists 604, 608, and 612 may specify solely a result processingcomponent, or specify both a result processing component and a speechrecognition processing component, or both a result processing componentand an audio capture component, or both an audio capture component and aspeech recognition processing component, or a result processingcomponent, audio capture component, and speech recognition processingcomponent.

As the preceding description indicates, any one or more of the lists604, 608, and 612 may specify zero components. Alternatively, forexample, the user context record 600 a may contain fewer than all of thelists 604, 608, and 612. For example, the user context record 600 a maycontain only list 604, only list 608, or only list 612. As anotherexample, the user context record 600 a may contain only list 604 and 608but not 612, or only list 604 and 612 but not 608, or only list 608 and612 but not 604. In such embodiments, the user's context may be definedby reference to only a single speech recognition component, or byreference to only two speech recognition components.

As another example, the result processing component list 612 may specifymultiple result processing components, indicating that the correspondinguser is associated with multiple result processing componentsconcurrently. For example, the user may use a first result processingcomponent on one computing device (e.g., a desktop computer) while usinga second result processing component on another computing device (e.g.,an Apple iPad tablet computer). Concurrently the speech recognitionprocessing component list 608 of the user context record 600 a may, forexample, specify a single speech recognition processing component. Inthis example, the single speech recognition processing component is usedto produce and provide speech recognition results to both of the resultprocessing components associated with the user. The results of thespeech recognition processing component may be provided to theappropriate result processing component, e.g., by the context sharingcomponent 250, in any of a variety of ways. For example, the speechrecognition processing component may identify the target applicationbased on the application context and set a flag that identifies thetarget application. The context sharing component 250 may then providethe speech recognition processing component's output to the resultprocessing component associated with (e.g., executing on the samemachine as) the target application identified by the flag.

Assume for purposes of example that the user 202 currently is using thesystem 200 a of FIG. 2A. As a result, the user context record 600 a mayinclude:

-   -   a record (in element 606 a) indicating that the audio capture        component 212 is to be used to perform audio capture on the        audio 208 generated from the user's speech 204;    -   a record (in element 610 a) indicating that the speech        recognition processing component 216 is to be used to perform        automatic speech recognition on the captured audio signal 214        produced by the audio capture component 212; and    -   a record (in element 614 a) indicating that the result        processing component 220 is to be used to process the results        218 generated by the speech recognition processing component        216.

Such records 606 a, 610 a, and 614 a may include any information whichis necessary or useful for enabling the appropriate input to be providedto the audio capture component 212, speech recognition processingcomponent 216, and result processing component 220, respectively. Suchinformation may include, for example, a location (e.g., IP address) ofthe component and/or a method of providing the appropriate input to thecomponent (e.g., local procedure call or network transmission).

The context sharing component 250 may gather information about thedevices and components currently accessible to the user 202 in any of avariety of ways. For example, FIG. 4 shows a dataflow diagram of asystem 400 for identifying the user's current context according to oneembodiment of the present invention. FIG. 5 shows a flowchart of amethod 500 performed by the system 400 of FIG. 4 according to oneembodiment of the present invention. User context may, however, beidentified, recorded, maintained, and processed in other ways.

Furthermore, although the system 400 of FIG. 4 includes a configurationthat is similar to that shown in FIG. 2A, this is merely an example andnot a limitation of the present invention. Although the system 400 ofFIG. 4 may include additional components from the system 200 a of FIG.2A, such as the microphone 206, which may operate in the same or similarmanner to that described in connection with FIG. 2, such components arenot shown in FIG. 4 for ease of illustration.

When the user 202 first begins to use a particular device, such as thedesktop computer 210 a in FIG. 4, or when the user 202 otherwise wishesto make speech recognition components on that device available for usein performing speech recognition on behalf of the user 202, the user 202may provide input 404 which causes the device (e.g., the desktopcomputer 210 a) to transmit information 406 about itself and the speechrecognition components on the device to the context sharing component250 (FIG. 5, operation 502). The user 202 may, for example, log into auser account maintained by the context sharing component 250, such as byproviding credentials (e.g., a unique username and password) to thecontext sharing component. In response to the user logging in to his orher account, the context sharing component may retrieve the devicecontext data 406 from the device, such as by using a context sharingclient 402 on the desktop computer 210 a to transmit data descriptive ofthe desktop computer 210 a and of the speech recognition componentslocated on the desktop computer 210 a (e.g., the audio capture component212 in the system 400 of FIG. 5).

The context sharing component 250 may update the context recordassociated with the user 202 to reflect the retrieved data, e.g., toreflect that the user 202 currently is using desktop computer 210 a,which contains and is executing audio capture component 212 (FIG. 5,operation 504). For example, assume that such an update results inupdating the user context record 600 a in FIG. 6 to indicate that theuser 202 currently is using audio capture component 212 on desktopcomputer 210 a. If the desktop computer 210 a were to include additionalspeech recognition components (such as a speech recognition processingcomponent and/or result processing component), information about suchcomponents would also be provided to the context sharing component 250and used by the context sharing component 250 to update the user'scontext record.

Operations 502 and 504 in FIG. 5 may be repeated for any number ofdevices currently being used by the user 202. For example, if the user202 is using an audio capture component on a mobile phone, while using aresult processing component on a terminal server, the user 202 may login to the context sharing component 250 through both such devices,thereby enabling the context sharing component 250 to determine that theuser 202 currently is using the audio capture component on the mobilephone and the result processing component on the terminal server.

The context sharing component 250 may associate with the user 202, andoptionally store in the user's context record 252, information aboutspeech recognition components which are not dependent on the user'scurrent context 252. For example, the user's current context record 252may indicate that the user's default result processing component is theresult processing component 220 located on the terminal server 230 a. Asa result, the context sharing component 250 may associate the resultprocessing component 220 with the user 202, independently of the user'scurrent context 252. One way in which this may be done is toautomatically include a record of the result processing component 220 inthe user's result processing component list 612 (FIG. 6A), whether ornot the user 202 or any of the user's devices provides an indication tothe context sharing component 250 that the user 202 currently isconnected to or otherwise using the terminal server 230 a.

The context sharing component 250 and/or context sharing client 402 mayuse various techniques to automatically identify speech recognitioncomponents currently available for use on behalf of the user 202. Forexample, the context sharing client 402 may detect that the user'scomputer 210 a currently is connected to the terminal server 230 a overa terminal services session, and thereby determine that the resultprocessing component 220 on the terminal server 230 a is available forprocessing speech recognition results on behalf of the user 202. Thecontext sharing client 402 may inform the context sharing component 250of such a determination, in response to which the context sharingcomponent 250 may record, in the user's context record 252, that theresult processing component 220 is available for use on behalf of theuser 202.

The context sharing component 250 may correlate the context informationstored in the user's context record 252 (e.g., the context record 600 aof FIG. 6A) to draw a conclusion that all components listed in theuser's context record 252 currently are available for use on behalf ofthe user 202 (operation 506). Once the context sharing component 250 hasidentified the current context of the user 202, including at least oneaudio capture component, speech recognition processing component, andresult processing component associated with the user 202, the contextsharing component 250 may be used to logically and/or physically connectsuch components to perform speech recognition (FIG. 5, operation 508).The techniques described above with respect to FIGS. 2A-2B and FIG. 3are examples of ways in which this dynamic creation of couplings amongspeech recognition components may be used to perform speech recognitionusing components which are distributed across multiple logical and/orphysical devices (FIG. 5, operation 510).

A particular user context record, such as the context record 600 a ofFIG. 6A, may remain valid and continue to be used, e.g., by the systems200 a-c of FIGS. 2A-2B, unless and until such a record changes orbecomes invalid for some reason. For example, the record 600 a maycontinue to be used, and treated as an accurate reflection of the user'scurrent context, unless and until the user logs out from the contextsharing component 250. In particular, if the user 202 logs out from thecontext sharing component 250 using a particular device, then thecontext sharing component may remove from the user's context record 600a all speech recognition components located on the particular device.Similarly, the context sharing component 250 may perform such removal inresponse to loss of a network connection with the particular device, orexpiration of a particular amount of time (e.g., 30 minutes) withouthaving contact with the particular device. As yet another example, thecontext record 600 a may be treated as an accurate reflection of theuser's current context unless and until the user 202 (or anadministrator or other user) expressly modifies the record 600 a.

As these examples illustrate, the user context record 600 a may reflectthe context of the user 202 at a particular point in time, or during aparticular range of times. As this implies, as the user's contextchanges over time, the user's context record 600 a may change over timein response to reflect the user's changing context. One particularlyuseful example of such modifications to the context record 600 a is toreflect changes in device usage by the user 202. For example, assumethat the context record 600 a indicates (in element 606 a) that the user202 is using the audio capture component 212 on the desktop computer inFIG. 2A at a first time. Then assume that at a second, later, time theuser 202 begins using a mobile phone connected to an audio capturecomponent in a speech recognition server, as in the system 200 b of FIG.2B. The user 202 may log in to the context sharing component 250 usingthe mobile phone, or otherwise cause the context sharing component 250to be informed that the audio capture component on the speechrecognition server 228 is now available for use on behalf of the user202 (FIG. 5, operation 512).

The context sharing component 250 may update the context recordassociated with the user 202 to reflect the retrieved data, e.g., toreflect that the user 202 currently is using the mobile phone connectedto the audio capture component 212 on the speech recognition server 228(FIG. 5, operation 514). This may, for example, result in removing therecord of the desktop computer audio capture component from the user'scontext record and replacing it with a record of the audio capturecomponent 212 on the speech recognition server 228, as shown in thecontext record 600 b of FIG. 6B, in which element 606 b represents theaudio capture component 212 on the speech recognition server 228.

For example, assume that such an update results in updating the usercontext record 600 a in FIG. 6 to indicate that the user 202 currentlyis using audio capture component 212 on desktop computer 210 a. If thedesktop computer 210 a were to include additional speech recognitioncomponents (such as a speech recognition processing component and/orresult processing component), information about such components wouldalso be provided to the context sharing component 250 and used by thecontext sharing component 250 to update the user's context record.

Therefore, although in the example of FIGS. 6A and 6B it is the audiocapture component associated with the user's current context whichchanges, this is merely an example. Additionally or alternatively, thespeech recognition processing component and/or result processingcomponent that is associated with the user's current context may changeover time. For example, a first speech recognition processing componentmay be associated with the user's current context at a first time, and asecond speech recognition processing component (which differs from thefirst speech recognition processing component) may be associated withthe user's current context at a second time. Similarly, a first resultprocessing component may be associated with the user's current contextat a first time, and a second result processing component (which differsfrom the first result processing component) may be associated with theuser's current context at a second time.

Once the user's context record has been updated to reflect the user'snew current context, the context sharing component 250 may correlate theupdated user context information (FIG. 5, operation 516), and logicallyand physically connect such components to perform speech recognition(FIG. 5, operation 518). The new set of speech recognition componentsassociated with the user 202 may then be used to perform speechrecognition on the user's behalf (FIG. 5, operation 520).

As the examples described above indicate, the context sharing component250 may identify the speech recognition components currently associatedwith the user 202 dynamically and at run-time. As the user's contextchanges, the context sharing component 250 may detect such changes andupdate the user's context record to reflect such changes. As a result,for example, the result processing component to which the contextsharing component 250 routes the speech recognition results 218 maychange automatically, dynamically, and at run-time, without the need tomodify any components of the system, such as the audio capture component212, speech recognition processing component 216, or result processingcomponent 220, and without the need to modify any devices in the system,such as the desktop computer 210 a, terminal server 230 a, or speechrecognition server 228.

The context sharing component 250, therefore, is an example of a meansfor dynamically coupling at least two speech recognition components toeach other. “Dynamically coupling” a first component to a secondcomponent refers to a process in which: (1) at a first time, the firstcomponent and the second component are not both associated with a user;(2) a state change occurs, as a result of which, at a second time, boththe first component and the second component are associated with theuser, such that results produced by one of the two components on behalfof the user are transmitted to the other of the two components.

Two coupled components (i.e., two components that are associated withthe same user) may communicate with each other directly or indirectly.An example of direct communication between two coupled components is onein which the context sharing component 250 is used to obtain informationabout the coupling being the two components, after which the twocomponents communicate directly with each other. For example, a resultprocessing component may request, from the context sharing component250, information about which speech recognition processing component iscurrently associated with a user, in response to which the contextsharing component 250 may provide such information to the requestingresult processing component. The result processing component may thenuse such information to communicate directly with the speech recognitionprocessing component identified by the context sharing component 250,without further involvement by the context sharing component. Directcommunication may, for example, be pull-based communication (i.e., inwhich one component requests data from the other component, whichresponds to the request with the requested data) or push-basedcommunication (i.e., in which one component provides data to the othercomponent but not in response to a request for such data from the othercomponent).

Alternatively, two coupled components may communicate with each otherindirectly through the context sharing component 250. For example, thecontext sharing component 250 may be used both to determine whichcomponents are currently coupled to each other and then to relay resultsfrom one coupled component to another in either or both directions. Forexample, one of the coupled components may provide output to the contextsharing component 250, which may then result that output to one or moreof the other coupled components.

The ability of embodiments of the present invention to dynamicallycouple speech recognition components to each other also implies that ata first time a first component may be coupled to a second component butnot to a third component, and that at a second time the first componentmay be coupled to the third component. At the second time the firstcomponent may or may not be coupled to the second component. In otherwords, if the first component is coupled to the second component, thencoupling the first component to the third component may or may notinvolve de-coupling the first component from the second component.

It is to be understood that although the invention has been describedabove in terms of particular embodiments, the foregoing embodiments areprovided as illustrative only, and do not limit or define the scope ofthe invention. Various other embodiments, including but not limited tothe following, are also within the scope of the claims. For example,elements and components described herein may be further divided intoadditional components or joined together to form fewer components forperforming the same functions.

FIGS. 2A-2B illustrate various examples of ways in which speechrecognition components (e.g., audio capture, speech recognitionprocessing, and result processing components) may be distributed acrossmultiple logical and/or physical devices, these are merely examples anddo not constitute limitations of the present invention. Speechrecognition components may be distributed across the kinds of devicesillustrated, and across other kinds of devices, in various other ways.

The context sharing component 250 may be implemented in any of a varietyof ways. For example, it may be located on any one or more of thedevices illustrated herein, such as the desktop computers 210 a-b,speech recognition server 228, and/or terminal server 230 a-b.Alternatively, for example, the context sharing component 250 may beimplemented on a logical and/or physical device distinct from those onwhich any of the speech recognition components reside. As anotherexample, the context sharing component 250 may be integrated, in wholeor in part, with one or more of the speech recognition components.

Furthermore, the context sharing component 250 is not required in allembodiments of the present invention. Rather, certain embodiments of thepresent invention may omit the context sharing component. For example,the Citrix Virtual Channels technology enables plugins to be installedto the client software (e.g., the terminal services client 232).Embodiments of the present invention may be implemented using such aplugin. For example, in one embodiment of the present invention, such aplugin is installed into the terminal services client 232 of FIG. 2B. Inthis embodiments, the context sharing component 250 may be omitted. Insuch an embodiment, the context matching described herein may beimplemented, for example, by using the audio capture component 213and/or the speech recognition processing component 216 to look for andfind a Virtual Channel of the present invention executing within theterminal services client 232 on the desktop computer 210 b. Uponlocating such a virtual channel, the audio capture component 213 and/orspeech recognition processing component 216 may communicate directlywith the plugin, thereby eliminating the need for the context sharingcomponent 250.

The structure and content of the user context records 600 a-b shown inFIGS. 6A-6B are merely examples and do not constitute limitations of thepresent invention. Rather, the functions performed by the user contextrecord may be implemented in other ways. For example, the user contextrecord may include a list of pairings, where each pairing pairs a deviceassociated with the user with a corresponding speech recognitioncomponent available on that device.

The techniques described above may be implemented, for example, inhardware, software tangibly stored on a computer-readable medium,firmware, or any combination thereof. The techniques described above maybe implemented in one or more computer programs executing on aprogrammable computer including a processor, a storage medium readableby the processor (including, for example, volatile and non-volatilememory and/or storage elements), at least one input device, and at leastone output device. Program code may be applied to input entered usingthe input device to perform the functions described and to generateoutput. The output may be provided to one or more output devices.

Each computer program within the scope of the claims below may beimplemented in any programming language, such as assembly language,machine language, a high-level procedural programming language, or anobject-oriented programming language. The programming language may, forexample, be a compiled or interpreted programming language.

Each such computer program may be implemented in a computer programproduct tangibly embodied in a machine-readable storage device forexecution by a computer processor. Method steps of the invention may beperformed by a computer processor executing a program tangibly embodiedon a computer-readable medium to perform functions of the invention byoperating on input and generating output. Suitable processors include,by way of example, both general and special purpose microprocessors.Generally, the processor receives instructions and data from a read-onlymemory and/or a random access memory. Storage devices suitable fortangibly embodying computer program instructions include, for example,all forms of non-volatile memory, such as semiconductor memory devices,including EPROM, EEPROM, and flash memory devices; magnetic disks suchas internal hard disks and removable disks; magneto-optical disks; andCD-ROMs. Any of the foregoing may be supplemented by, or incorporatedin, specially-designed ASICs (application-specific integrated circuits)or FPGAs (Field-Programmable Gate Arrays). A computer can generally alsoreceive programs and data from a storage medium such as an internal disk(not shown) or a removable disk. These elements will also be found in aconventional desktop or workstation computer as well as other computerssuitable for executing computer programs implementing the methodsdescribed herein, which may be used in conjunction with any digitalprint engine or marking engine, display monitor, or other raster outputdevice capable of producing color or gray scale pixels on paper, film,display screen, or other output medium.

What is claimed is:
 1. A system comprising: an audio capture component,the audio capture component comprising means for capturing a first audiosignal representing first speech of a user to produce a first capturedaudio signal; a speech recognition processing component comprising meansfor performing automatic speech recognition on the first captured audiosignal to produce first speech recognition results; a first resultprocessing component, the first result processing component comprisingfirst means for processing the first speech recognition results toproduce first result output; a second result processing component, thesecond result processing component comprising second means forprocessing the first speech recognition results to produce second resultoutput; a context sharing component comprising means for identifying afirst one of the first and second result processing components as beingassociated with a first context of the user at a first time; and speechrecognition result provision means for providing the first speechrecognition results to the identified first one of the first and secondresult processing components.
 2. The system of claim 1, wherein: theaudio capture component further comprises means for capturing a secondaudio signal representing second speech of the user to produce a secondcaptured audio signal; the speech recognition processing componentfurther comprises means for performing automatic speech recognition onthe second captured audio signal to produce second speech recognitionresults; the context sharing component further comprises means foridentifying a second one of the first and second result processingcomponents as being associated with a second context of the user at asecond time, wherein the second one of the first and second resultprocessing components differs from the first one of the first and secondresult processing components; and wherein the speech recognition resultprovision means further comprises means for providing the second speechrecognition results to the identified second one of the first and secondresult processing components.
 3. A computer-implemented method for usewith a system: wherein the system comprises: an audio capture component;a speech recognition processing component; a first result processingcomponent; a second result processing component; a context sharingcomponent; and speech recognition result provision means; wherein themethod comprises: (A) using the audio capture component to capture afirst audio signal representing first speech of a user to produce afirst captured audio signal; (B) using the speech recognition processingcomponent to perform automatic speech recognition on the first capturedaudio signal to produce first speech recognition results; (C) using thefirst result processing component to process the first speechrecognition results to produce first result output; (D) using secondresult processing component to process the first speech recognitionresults to produce second result output; (E) using the context sharingcomponent to identify a first one of the first and second resultprocessing components as being associated with a first context of theuser at a first time; (F) using the speech recognition result provisionmeans to provide the first speech recognition results to the identifiedfirst one of the first and second result processing components.
 4. Themethod of claim 3, further comprising: (G) using the audio capturecomponent to capture a second audio signal representing second speech ofthe user to produce a second captured audio signal; (H) using the speechrecognition processing component to perform automatic speech recognitionon the second captured audio signal to produce second speech recognitionresults; (I) using the context sharing component to identify a secondone of the first and second result processing components as beingassociated with a second context of the user at a second time, whereinthe second one of the first and second result processing componentsdiffers from the first one of the first and second result processingcomponents; and (J) using the speech recognition result provision meansto provide the second speech recognition results to the identifiedsecond one of the first and second result processing components.
 5. Asystem comprising: a first audio capture component comprising firstmeans for capturing a first audio signal representing speech of a userto produce a first captured audio signal; a first speech recognitionprocessing component comprising first means for performing automaticspeech recognition on the first captured audio signal to produce firstspeech recognition results; a first result processing componentcomprising first means for processing the first speech recognitionresults to produce first result output; a context sharing componentcomprising means for dynamically coupling at least two of the firstaudio capture component, the first speech recognition processingcomponent, and the first result processing component to each other. 6.The system of claim 5, further comprising a first device, wherein thefirst device comprises the first audio capture component, and a seconddevice, wherein the second device includes the first result processingcomponent, wherein the first device is distinct from the second device.7. The system of claim 5, wherein the context sharing componentcomprises means for dynamically coupling the first audio capturecomponent to the first speech recognition processing component.
 8. Thesystem of claim 7: further comprising a second audio capture componentcomprising second means for capturing a second audio signal representingspeech of a user to produce a second captured audio signal; and whereinthe context sharing component further comprises means for dynamicallycoupling the second audio capture component to the first speechrecognition processing component.
 9. The system of claim 7, furthercomprising: means for providing the first captured audio signal to thefirst speech recognition processing component after dynamically couplingthe first audio capture component to the first speech recognitionprocessing component.
 10. The system of claim 9, wherein the contextsharing component comprises the means for providing the first capturedaudio signal.
 11. The system of claim 9, wherein the first audio capturecomponent comprises the means for providing the first captured audiosignal.
 12. The system of claim 9, wherein the first speech recognitionprocessing component comprises the means for providing the firstcaptured audio signal.
 13. The system of claim 9, wherein the means forproviding comprises means for providing the first captured audio signalto the first speech recognition processing component in real-time. 14.The system of claim 5, wherein the context sharing component comprisesmeans for dynamically coupling the first audio capture component to thefirst result processing component.
 15. The system of claim 5, whereinthe context sharing component comprises means for dynamically couplingthe first speech recognition processing component to the first resultprocessing component.
 16. The system of claim 15: further comprising asecond speech recognition processing component comprising second meansfor performing automatic speech recognition on the first captured audiosignal to produce second speech recognition results; and wherein thecontext sharing component further comprises means for dynamicallycoupling the first audio capture component to the second speechrecognition processing component.
 17. The system of claim 15, furthercomprising: means for providing the first speech recognition results tothe first result processing component after dynamically coupling thefirst speech recognition processing component to the first resultprocessing component.
 18. The system of claim 17, wherein the contextsharing component comprises the means for providing the first speechrecognition results.
 19. The system of claim 17, wherein the firstspeech recognition processing component comprises the means forproviding the first speech recognition results.
 20. The system of claim17, wherein the first result processing component comprises the meansfor providing the first speech recognition results.
 21. The system ofclaim 17, wherein the means for providing comprises means for providingthe first speech recognition results to the first result processingcomponent in real-time.
 22. The system of claim 5, wherein the contextsharing component comprises means for dynamically coupling the firstaudio capture component to the first speech recognition processingcomponent and for dynamically coupling the first speech recognitionprocessing component to the first result processing component.
 23. Thesystem of claim 5, wherein the means for dynamically coupling comprisesmeans for dynamically coupling at least two of the first audio capturecomponent, the first speech recognition processing component, and thefirst result processing component to each other at run-time.
 24. Acomputer-implemented method for us with a system: wherein the systemcomprises: a first audio capture component; a first speech recognitionprocessing component; a first result processing component; and a contextsharing component; wherein the method comprises: (A) using the firstaudio capture component to capture a first audio signal representingspeech of a user to produce a first captured audio signal; (B) using thefirst speech recognition processing component to perform automaticspeech recognition on the first captured audio signal to produce firstspeech recognition results; (C) using the first result processingcomponent to process the first speech recognition results to producefirst result output; (D) using the context sharing component todynamically couple at least two of the first audio capture component,the first speech recognition processing component, and the first resultprocessing component to each other.
 25. The method of claim 24: whereinthe system further comprises: a first device, wherein the first devicecomprises the first audio capture component; and a second device,wherein the second device includes the first result processingcomponent; wherein the first device is distinct from the second device.26. The method of claim 24, wherein (D) comprises dynamically couplingthe first audio capture component to the first speech recognitionprocessing component.
 27. The method of claim 24: wherein the systemfurther comprises a second audio capture component; wherein the methodfurther comprises: (E) using the second audio capture component tocapture a second audio signal representing speech of a user to produce asecond captured audio signal; and wherein (D) comprises dynamicallycoupling the second audio capture component to the first speechrecognition processing component.
 28. The method of claim 26, furthercomprising: (E) providing the first captured audio signal to the firstspeech recognition processing component after dynamically coupling thefirst audio capture component to the first speech recognition processingcomponent.
 29. The method of claim 28, wherein (E) is performed by thecontext sharing component.
 30. The method of claim 28, wherein (E) isperformed by the first audio capture component.
 31. The system of claim28, wherein (E) is performed by the first speech recognition processingcomponent.
 32. The method of claim 28, wherein (E) comprises providingthe first captured audio signal to the first speech recognitionprocessing component in real-time.
 33. The method of claim 24, wherein(D) comprises dynamically coupling the first audio capture component tothe first result processing component.
 34. The method of claim 24,wherein (D) comprises dynamically coupling the first speech recognitionprocessing component to the first result processing component.
 35. Themethod of claim 34: wherein the system further comprises a second speechrecognition processing component; and wherein the method furthercomprises: (E) performing automatic speech recognition on the firstcaptured audio signal to produce second speech recognition results; andwherein (D) comprises dynamically coupling the first audio capturecomponent to the second speech recognition processing component.
 36. Themethod of claim 34, further comprising: (F) providing the first speechrecognition results to the first result processing component afterdynamically coupling the first speech recognition processing componentto the first result processing component.
 37. The method of claim 36,wherein (F) is performed by the context sharing component.
 38. Themethod of claim 36, wherein (F) is performed by the first speechrecognition processing component.
 39. The method of claim 36, wherein(F) is performed by the first result processing component.
 40. Themethod of claim 36, wherein (F) comprises providing the first speechrecognition results to the first result processing component inreal-time.
 41. The method of claim 24, wherein (D) comprises dynamicallycoupling the first audio capture component to the first speechrecognition processing component and dynamically coupling the firstspeech recognition processing component to the first result processingcomponent.
 42. The method of claim 24, wherein (D) comprises dynamicallycoupling at least two of the first audio capture component, the firstspeech recognition processing component, and the first result processingcomponent to each other at run-time.